Improved Motion Classification With an Integrated Multimodal Exoskeleton Interface - Langlois, Kevin; Geeroms, Joost; Van De Velde, Gabriel; Rodriguez-Guerrero, Carlos; Verstraten, Tom; Vanderborght, Bram; Lefeber, Dirk (Frontiers in Neurorobotics, 2021)
Human motion intention detection is an essential part of the control of upper-body exoskeletons. While surface electromyography (sEMG)-based systems may be able to provide anticipatory control, they typically require exact placement of the electrodes on the muscle bodies which limits the practical use and donning of the technology. In this study, we propose a K-Nearest Neighbours classifier to detect reaching movements and lifting tasks that represent daily activities of industrial workers. The classifier uses multimodal measurements obtained from 3D-printed sEMG- and pressure-sensors made of flexible, conductive materials, fully integrated in a 3D-printed physical interface for the upper arm. The performance of the classifier is validated through repeated experiments and compared to a unimodal EMG-based classifier. The results indicate that excellent prediction performance can be obtained, even with a minimal amount of sEMG electrodes and without specific placement of the electrode.